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Closer Relationships Between Sponsors and Regulators Could Transform Clinical Trials

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Article

Closer Relationships Between Sponsors and Regulators Could Transform Clinical Trials

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The following article is an opinion piece written by Dr. Gen Li. The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position of Technology Networks.

The pharmaceutical industry and medicines regulators have the same end goal in clinical development – to produce safe and innovative therapies to help patients. Both pharmaceutical companies and regulators have collaborated closely over the course of the COVID-19 pandemic to develop vaccines, diagnostic tests and other therapies quicker than ever before and have shown us that clinical development can be accelerated. However, there are still several long-standing barriers to progress, when the focus isn’t on a global pandemic, that must now be remedied.

To optimize clinical trials and maintain this progress sponsors must now make better use of the data available to them, to be able to make informed decisions about protocol design (including use of synthetic control arms and digital twins to reduce patient burden), enrollment planning, and investigator site selection and quickly report back to regulators. To facilitate this there are three key elements to be addressed: we need to improve communication between regulators and sponsors through better use of data; improve the transparency in reporting clinical trial outcomes; and finally, implement synthetic data, digital twins and synthetic control arms.

1. Removing the disconnect between industry and regulators


Regulators have shown unprecedented levels of flexibility to enable treatments to be assessed rapidly. For instance, during the first few weeks of the pandemic, the UK’s Medicines and Healthcare products Regulatory Agency (MHRA) operated a triage system to pre-evaluate potential products and allowed adaptive clinical trials to be used. In a similar vein, the US Food and Drug Administration (FDA) granted pre-investigational new drug (IND) meetings in less than 30 days and has issued over 74 other COVID-specific guidance documents. Following this success, patients and governments, payers and payees, will also now begin asking why other drugs, like much-needed oncology breakthroughs, or rare diseases treatments, can’t be developed as quickly. We now need to make sure that these positive outcomes from the pandemic become part of the long-term DNA.

Yet, in many traditional trials there is still a disconnect between what regulators are asking for and the insights sponsors are able to provide into their ongoing trials. For example, between Phase 2 and Phase 3 testing, a regulator may request specific and detailed inclusion/exclusion criteria to be altered in the trial. This would then enable them to understand the impact of a drug in the full patient population, but sponsors and CROs often lack the data intelligence to understand how and where these patients can be recruited efficiently and often turn to historical, incongruous and irrelevant. In a transparent, big-data-driven, artificial intelligence-enabled clinical data science platform, it is now entirely feasible for a sponsor and regulatory authority to make objective, optimized decisions on these once very sticky matters.

In order to deliver on what regulators require to sign off drugs more quickly and overcome other obstacles, not only do we need access to reliable and real-time data, that covers the four dimensions – volume, variety, velocity and veracity – but the industry as a whole needs to collaborate and take advantage of the rolling review approach. This has been crucial during COVID-19 to accelerate the development of vaccines, including Oxford–AstraZeneca’s (ChAdOx1-S [recombinant]), as regulators could obtain trial data when and where available to fast-track analyses. With this data-driven approach, it then enables sponsors to optimize their protocols, improve investigator site selection and gather data for clinical trial reports objectively in real-time, rather than on a perception-led basis. Other, similar innovative approaches to approval will need to be explored by agencies, and international authorities must develop closer relationships with each other to share research and reports.

2. Transparency in reporting outcomes


Recently there has also been a call for increased transparency in the pharmaceutical industry on how it reports clinical trial data. The World Health Organization (WHO) and the International Coalition of Medicines Regulatory Authorities (ICMRA) in a joint statement cited the need for “wide access to clinical data for all new medicines and vaccines”. When negative trials are not published, the scientific literature is incomplete. This can lead to further downstream problems when researchers and policy analysts conduct systematic reviews of available evidence to weigh benefit and risk for therapies; if negative data are not found in the literature, this will skew any assessments to look more positive than the reality. Much of our gained knowledge in clinical data sciences in the past decade or so by at large is derived from improved data transparency. But this is not enough, and more efforts are warranted.

By reporting negative trial outcomes, the more complete information will not only make future developments easier in all areas of drug discovery, not just for the target originally identified, but could also help to increase public trust in the pharmaceutical industries, which has been eroded over the last few decades.

3. Implementing synthetic data to revolutionize trials


As companies move to a data-driven approach and work more closely with regulators they will also be able to explore innovative approaches to conducting clinical trials. For example, digital twins and synthetic comparator arms – while they have been discussed for many years, we now have the data and analytical knowledge to implement them. Digital twins and synthetic comparator arms use data collated from similar or identical trials or research using the same agent, with real-world patient data, to accurately model comparator outcomes and the patient journey.

Traditional placebo arms increase the burden on both patients and sites. They are not only associated with ethical challenges, but also require higher numbers of patients to complete trials. Currently, patients avoid clinical trials altogether because they fear being placed in a placebo group; in addition, a lack of efficacy is one of the main reasons for a patient dropping out of a trial, so inevitably those receiving the placebo are more likely to drop out. Eliminating or reducing the size of the placebo arm could therefore improve trial compliance and participation.

Ironically, the most daunting challenge in implementing digital twins and synthetic comparator arms are not coming from constructing a set of safety and efficacy profiles based on available data with needed statistical confidence, but from the human confidence in this approach of the momentum of the current gold standard: a randomized, controlled trial design.

To make this a reality, sponsors need to work closely with regulators, clinicians and patients to build confidence in the new approach and collaborate on its development. These changes should also be made in responsible and iterative ways, but it’s imperative we begin to implement changes and move forward.

Ultimately, remaining productive and innovative during this current period will be heavily reliant on an ability to make sense of, and be led by, data. If sponsors and regulators are able to work in a more connected way, with the implementation of rolling reviews, it will enable the industry to move forward and address underlying issues of spiraling costs and delayed therapies, which have been plaguing trials for years. Patients are waiting.

 

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